


import os

import jsonlines
import numpy as np
from tqdm import tqdm


A				:       List[str]      =  2048
A				:       Any      =  4096
A				:       int      =  42
A				:       Optional[Any]      =  os.environ.pop('PROCESS_TRAIN', 'false')
A				:       Tuple      =  {"""null""": 0, """short""": 1, """long""": 2, """yes""": 3, """no""": 4}





def 						_lowerCAmelCase							(     _lowerCAmelCase			)    ->						Any:


						'''simple docstring'''






						def choose_first(_lowerCAmelCase   ,						_lowerCAmelCase=False			):
												assert isinstance(snake_case__   ,						snake_case__			)
												if len(snake_case__			) == 1:
																		__snake_case				=		answer[0]
																		return {k: [answer[k]] for k in answer} if is_long_answer else answer
												for a in answer:
																		if is_long_answer:
																								__snake_case				=		{k: [a[k]] for k in a}
																		if len(a["start_token"]			) > 0:
																								break
												return a

						__snake_case				=		{"id": example["id"]}
						__snake_case				=		example["annotations"]
						__snake_case				=		annotation["yes_no_answer"]
						if 0 in yes_no_answer or 1 in yes_no_answer:
												__snake_case				=		["yes"] if 1 in yes_no_answer else ["no"]
												__snake_case				=		__snake_case				=		[]
												__snake_case				=		__snake_case				=		[]
												__snake_case				=		["<cls>"]
						else:
												__snake_case				=		["short"]
												__snake_case				=		choose_first(annotation["short_answers"]			)
												if len(out["start_token"]			) == 0:
																		# answer will be long if short is not available
																		__snake_case				=		["long"]
																		__snake_case				=		choose_first(annotation["long_answer"]   ,						is_long_answer=snake_case__			)
																		__snake_case				=		[]
												answer.update(snake_case__			)

						# disregard some samples
						if len(answer["start_token"]			) > 1 or answer["start_token"] == answer["end_token"]:
												__snake_case				=		True
						else:
												__snake_case				=		False

						__snake_case				=		["start_token", "end_token", "start_byte", "end_byte", "text"]
						if not all(isinstance(answer[k]   ,						snake_case__			) for k in cols			):
												raise ValueError("Issue in ID"   ,						example["id"]			)

						return answer





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase=False			)    ->						Tuple:


						'''simple docstring'''






						__snake_case				=		_get_single_answer(snake_case__			)
						# bytes are of no use
						del answer["start_byte"]
						del answer["end_byte"]

						# handle yes_no answers explicitly
						if answer["category"][0] in ["yes", "no"]:  # category is list with one element
												__snake_case				=		example["document"]["tokens"]
												__snake_case				=		[]
												for i in range(len(doc["token"]			)			):
																		if not doc["is_html"][i]:
																								context.append(doc["token"][i]			)
												return {
												    "context": " ".join(snake_case__			),
												    "answer": {
												        "start_token": -100,  # ignore index in cross-entropy
												        "end_token": -100,  # ignore index in cross-entropy
												        "category": answer["category"],
												        "span": answer["category"],  # extra
												    },
												}

						# later, help in removing all no answers
						if answer["start_token"] == [-1]:
												return {
												    "context": "None",
												    "answer": {
												        "start_token": -1,
												        "end_token": -1,
												        "category": "null",
												        "span": "None",  # extra
												    },
												}

						# handling normal samples

						__snake_case				=		["start_token", "end_token"]
						answer.update({k: answer[k][0] if len(answer[k]			) > 0 else answer[k] for k in cols}			)  # e.g. [10] == 10

						__snake_case				=		example["document"]["tokens"]
						__snake_case				=		answer["start_token"]
						__snake_case				=		answer["end_token"]

						__snake_case				=		[]
						for i in range(len(doc["token"]			)			):
												if not doc["is_html"][i]:
																		context.append(doc["token"][i]			)
												else:
																		if answer["start_token"] > i:
																								start_token -= 1
																		if answer["end_token"] > i:
																								end_token -= 1
						__snake_case				=		" ".join(context[start_token:end_token]			)

						# checking above code
						if assertion:
												__snake_case				=		doc["is_html"][answer["start_token"] : answer["end_token"]]
												__snake_case				=		doc["token"][answer["start_token"] : answer["end_token"]]
												__snake_case				=		" ".join([old[i] for i in range(len(snake_case__			)			) if not is_html[i]]			)
												if new != old:
																		print("ID:"   ,						example["id"]			)
																		print("New:"   ,						snake_case__   ,						end="\n"			)
																		print("Old:"   ,						snake_case__   ,						end="\n\n"			)

						return {
						    "context": " ".join(snake_case__			),
						    "answer": {
						        "start_token": start_token,
						        "end_token": end_token - 1,  # this makes it inclusive
						        "category": answer["category"],  # either long or short
						        "span": new,  # extra
						    },
						}





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase=2048   ,						_lowerCAmelCase=4096   ,						_lowerCAmelCase=True			)    ->						Any:


						'''simple docstring'''






						__snake_case				=		get_context_and_ans(snake_case__   ,						assertion=snake_case__			)
						__snake_case				=		out["answer"]

						# later, removing these samples
						if answer["start_token"] == -1:
												return {
												    "example_id": example["id"],
												    "input_ids": [[-1]],
												    "labels": {
												        "start_token": [-1],
												        "end_token": [-1],
												        "category": ["null"],
												    },
												}

						__snake_case				=		tokenizer(example["question"]["text"]   ,						out["context"]			).input_ids
						__snake_case				=		input_ids.index(tokenizer.sep_token_id			) + 1

						# return yes/no
						if answer["category"][0] in ["yes", "no"]:  # category is list with one element
												__snake_case				=		[]
												__snake_case				=		[]
												__snake_case				=		input_ids[:q_len]
												__snake_case				=		range(snake_case__   ,						len(snake_case__			)   ,						max_length - doc_stride			)
												for i in doc_start_indices:
																		__snake_case				=		i + max_length - q_len
																		__snake_case				=		input_ids[i:end_index]
																		inputs.append(q_indices + slice			)
																		category.append(answer["category"][0]			)
																		if slice[-1] == tokenizer.sep_token_id:
																								break

												return {
												    "example_id": example["id"],
												    "input_ids": inputs,
												    "labels": {
												        "start_token": [-100] * len(snake_case__			),
												        "end_token": [-100] * len(snake_case__			),
												        "category": category,
												    },
												}

						__snake_case				=		out["context"].split()
						__snake_case				=		splitted_context[answer["end_token"]]
						__snake_case				=		len(
						    tokenizer(
						        " ".join(splitted_context[: answer["start_token"]]			)   ,						add_special_tokens=snake_case__   ,						).input_ids			)
						__snake_case				=		len(
						    tokenizer(" ".join(splitted_context[: answer["end_token"]]			)   ,						add_special_tokens=snake_case__			).input_ids			)

						answer["start_token"] += q_len
						answer["end_token"] += q_len

						# fixing end token
						__snake_case				=		len(tokenizer(snake_case__   ,						add_special_tokens=snake_case__			).input_ids			)
						if num_sub_tokens > 1:
												answer["end_token"] += num_sub_tokens - 1

						__snake_case				=		input_ids[answer["start_token"] : answer["end_token"] + 1]  # right & left are inclusive
						__snake_case				=		answer["start_token"]
						__snake_case				=		answer["end_token"]

						if assertion:
												__snake_case				=		tokenizer.decode(snake_case__			)
												if answer["span"] != new:
																		print("ISSUE IN TOKENIZATION"			)
																		print("OLD:"   ,						answer["span"]			)
																		print("NEW:"   ,						snake_case__   ,						end="\n\n"			)

						if len(snake_case__			) <= max_length:
												return {
												    "example_id": example["id"],
												    "input_ids": [input_ids],
												    "labels": {
												        "start_token": [answer["start_token"]],
												        "end_token": [answer["end_token"]],
												        "category": answer["category"],
												    },
												}

						__snake_case				=		input_ids[:q_len]
						__snake_case				=		range(snake_case__   ,						len(snake_case__			)   ,						max_length - doc_stride			)

						__snake_case				=		[]
						__snake_case				=		[]
						__snake_case				=		[]
						__snake_case				=		[]  # null, yes, no, long, short
						for i in doc_start_indices:
												__snake_case				=		i + max_length - q_len
												__snake_case				=		input_ids[i:end_index]
												inputs.append(q_indices + slice			)
												assert len(inputs[-1]			) <= max_length, "Issue in truncating length"

												if start_token >= i and end_token <= end_index - 1:
																		__snake_case				=		start_token - i + q_len
																		__snake_case				=		end_token - i + q_len
																		answers_category.append(answer["category"][0]			)  # ["short"] -> "short"
												else:
																		__snake_case				=		-100
																		__snake_case				=		-100
																		answers_category.append("null"			)
												__snake_case				=		inputs[-1][start_token : end_token + 1]

												answers_start_token.append(snake_case__			)
												answers_end_token.append(snake_case__			)
												if assertion:
																		if new != old and new != [tokenizer.cls_token_id]:
																								print("ISSUE in strided for ID:"   ,						example["id"]			)
																								print("New:"   ,						tokenizer.decode(snake_case__			)			)
																								print("Old:"   ,						tokenizer.decode(snake_case__			)   ,						end="\n\n"			)
												if slice[-1] == tokenizer.sep_token_id:
																		break

						return {
						    "example_id": example["id"],
						    "input_ids": inputs,
						    "labels": {
						        "start_token": answers_start_token,
						        "end_token": answers_end_token,
						        "category": answers_category,
						    },
						}





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase   ,						_lowerCAmelCase=2048   ,						_lowerCAmelCase=4096   ,						_lowerCAmelCase=False			)    ->						str:


						'''simple docstring'''






						__snake_case				=		get_strided_contexts_and_ans(
						    snake_case__   ,						snake_case__   ,						doc_stride=snake_case__   ,						max_length=snake_case__   ,						assertion=snake_case__   ,						)

						return example





def 						_lowerCAmelCase							(     _lowerCAmelCase   ,						_lowerCAmelCase			)    ->						Tuple:


						'''simple docstring'''






						with jsonlines.open(snake_case__   ,						"a"			) as writer:
												for example in tqdm(snake_case__   ,						total=len(snake_case__			)   ,						desc="Saving samples ... "			):
																		__snake_case				=		example["labels"]
																		for ids, start, end, cat in zip(
																		    example["input_ids"]   ,						labels["start_token"]   ,						labels["end_token"]   ,						labels["category"]   ,						):
																								if start == -1 and end == -1:
																														continue  # leave waste samples with no answer
																								if cat == "null" and np.random.rand() < 0.6:
																														continue  # removing 50 % samples
																								writer.write(
																								    {
																								        "input_ids": ids,
																								        "start_token": start,
																								        "end_token": end,
																								        "category": CATEGORY_MAPPING[cat],
																								    }			)


if __name__ == "__main__":
	from datasets import load_dataset

	from transformers import BigBirdTokenizer

	A				:       Optional[int]      =  load_dataset('natural_questions')
	A				:       Optional[Any]      =  BigBirdTokenizer.from_pretrained('google/bigbird-roberta-base')

	A				:       Dict      =  data["""train""" if PROCESS_TRAIN == """true""" else """validation"""]

	A				:       int      =  {
	    """tokenizer""": tokenizer,
	    """doc_stride""": DOC_STRIDE,
	    """max_length""": MAX_LENGTH,
	    """assertion""": False,
	}
	A				:       Optional[Any]      =  data.map(prepare_inputs, fn_kwargs=fn_kwargs)
	A				:       Dict      =  data.remove_columns(['annotations', 'document', 'id', 'question'])
	print(data)

	np.random.seed(SEED)
	A				:       List[str]      =  """nq-training.jsonl""" if PROCESS_TRAIN == """true""" else """nq-validation.jsonl"""
	save_to_disk(data, file_name=cache_file_name)











"""simple docstring"""




import os
from collections import namedtuple

import pytest

from datasets import ClassLabel, Features, Sequence, Value
from datasets.commands.test import TestCommand
from datasets.info import DatasetInfo, DatasetInfosDict


lowerCAmelCase				:     Dict					=				namedtuple(
    """_TestCommandArgs""",
    [
        """dataset""",
        """name""",
        """cache_dir""",
        """data_dir""",
        """all_configs""",
        """save_infos""",
        """ignore_verifications""",
        """force_redownload""",
        """clear_cache""",
    ],
    defaults=[None, None, None, False, False, False, False, False],
)



def        a__					(							snake_case__			,					snake_case__							)		->   Optional[Any]:
			return (abs(source - target							) / target) < 0.01



@pytest.mark.integration
def        a__					(							snake_case__							)		->   int:
			lowerCamelCase      =   _TestCommandArgs(dataset=snake_case__			,					all_configs=snake_case__			,					save_infos=snake_case__							)
			lowerCamelCase      =   TestCommand(*snake_case__							)
			test_command.run()
			lowerCamelCase      =   os.path.join(snake_case__			,					"""README.md"""							)
			assert os.path.exists(snake_case__							)
			lowerCamelCase      =   DatasetInfosDict.from_directory(snake_case__							)
			lowerCamelCase      =   DatasetInfosDict(
			    {
			        """default""": DatasetInfo(
			            features=Features(
			                {
			                    """tokens""": Sequence(Value("""string"""							)							),
			                    """ner_tags""": Sequence(
			                        ClassLabel(names=["""O""", """B-PER""", """I-PER""", """B-ORG""", """I-ORG""", """B-LOC""", """I-LOC"""]							)							),
			                    """langs""": Sequence(Value("""string"""							)							),
			                    """spans""": Sequence(Value("""string"""							)							),
			                }							)			,					splits=[
			                {
			                    """name""": """train""",
			                    """num_bytes""": 2_35_15_63,
			                    """num_examples""": 1_00_00,
			                },
			                {
			                    """name""": """validation""",
			                    """num_bytes""": 23_84_18,
			                    """num_examples""": 10_00,
			                },
			            ]			,					download_size=3_94_06_80			,					dataset_size=2_58_99_81			,					)
			    }							)
			assert dataset_infos.keys() == expected_dataset_infos.keys()
			for key in DatasetInfo._INCLUDED_INFO_IN_YAML:
						lowerCamelCase ,  lowerCamelCase      =   getattr(dataset_infos["""default"""]			,					snake_case__							), getattr(expected_dataset_infos["""default"""]			,					snake_case__							)
						if key == "num_bytes":
									assert is_apercent_close(snake_case__			,					snake_case__							)
						elif key == "splits":
									assert list(snake_case__							) == list(snake_case__							)
									for split in result:
												assert result[split].name == expected[split].name
												assert result[split].num_examples == expected[split].num_examples
												assert is_apercent_close(result[split].num_bytes			,					expected[split].num_bytes							)
						else:
									result == expected



